An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design
This paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assiste...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/8/11/2030 |
| _version_ | 1818153433826328576 |
|---|---|
| author | Qinghua Deng Shuai Shao Lei Fu Haifeng Luan Zhenping Feng |
| author_facet | Qinghua Deng Shuai Shao Lei Fu Haifeng Luan Zhenping Feng |
| author_sort | Qinghua Deng |
| collection | DOAJ |
| description | This paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assisted self-learning artificial neural network and a dynamic sampling database. The 3-D multidisciplinary optimization approach was validated by the original T-100 turbine and the T-100re turbine obtained from the automated preliminary design approach, for maximizing the total-to-static efficiency and minimizing the rotor weight while keeping the mass flow rate constant and stress limitation satisfied. The validation results indicate that the total-to-static efficiency is 89.6%, increased by 1.3%, and the rotor weight is reduced by 0.14 kg (14.6%) based on the T-100re turbine, while the efficiency is 88.2%, increased by 2.2% and the weight is reduced by 0.49 kg (37.4%) based on the original T-100 turbine. Moreover, the T-100re turbine shows better performance at the preliminary design stage and conserves this advantage to the end, though both the aerodynamic performance of the T-100 and the T-100re turbine are improved after 3-D optimization. At the same time, it is implied that the preliminary design plays an essential role in the radial inflow turbine design process, and it is hard for only 3-D optimization to get a further performance improvement. |
| first_indexed | 2024-12-11T14:10:33Z |
| format | Article |
| id | doaj.art-7790ca75be1b4f169727eaadb777b09c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T14:10:33Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7790ca75be1b4f169727eaadb777b09c2022-12-22T01:03:28ZengMDPI AGApplied Sciences2076-34172018-10-01811203010.3390/app8112030app8112030An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization DesignQinghua Deng0Shuai Shao1Lei Fu2Haifeng Luan3Zhenping Feng4Shaanxi Engineering Laboratory of Turbomachinery and Power Equipment, Institute of Turbomachinery, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaChina Shipbuilding New Power Co., Ltd., Beijing 100097, ChinaShaanxi Engineering Laboratory of Turbomachinery and Power Equipment, Institute of Turbomachinery, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaChina Shipbuilding New Power Co., Ltd., Beijing 100097, ChinaShaanxi Engineering Laboratory of Turbomachinery and Power Equipment, Institute of Turbomachinery, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThis paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assisted self-learning artificial neural network and a dynamic sampling database. The 3-D multidisciplinary optimization approach was validated by the original T-100 turbine and the T-100re turbine obtained from the automated preliminary design approach, for maximizing the total-to-static efficiency and minimizing the rotor weight while keeping the mass flow rate constant and stress limitation satisfied. The validation results indicate that the total-to-static efficiency is 89.6%, increased by 1.3%, and the rotor weight is reduced by 0.14 kg (14.6%) based on the T-100re turbine, while the efficiency is 88.2%, increased by 2.2% and the weight is reduced by 0.49 kg (37.4%) based on the original T-100 turbine. Moreover, the T-100re turbine shows better performance at the preliminary design stage and conserves this advantage to the end, though both the aerodynamic performance of the T-100 and the T-100re turbine are improved after 3-D optimization. At the same time, it is implied that the preliminary design plays an essential role in the radial inflow turbine design process, and it is hard for only 3-D optimization to get a further performance improvement.https://www.mdpi.com/2076-3417/8/11/2030radial inflow turbineevolutionary algorithmgenetic algorithmartificial neural networkmultidisciplinary optimization |
| spellingShingle | Qinghua Deng Shuai Shao Lei Fu Haifeng Luan Zhenping Feng An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design Applied Sciences radial inflow turbine evolutionary algorithm genetic algorithm artificial neural network multidisciplinary optimization |
| title | An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design |
| title_full | An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design |
| title_fullStr | An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design |
| title_full_unstemmed | An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design |
| title_short | An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design |
| title_sort | integrated design and optimization approach for radial inflow turbines part ii multidisciplinary optimization design |
| topic | radial inflow turbine evolutionary algorithm genetic algorithm artificial neural network multidisciplinary optimization |
| url | https://www.mdpi.com/2076-3417/8/11/2030 |
| work_keys_str_mv | AT qinghuadeng anintegrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT shuaishao anintegrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT leifu anintegrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT haifengluan anintegrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT zhenpingfeng anintegrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT qinghuadeng integrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT shuaishao integrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT leifu integrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT haifengluan integrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign AT zhenpingfeng integrateddesignandoptimizationapproachforradialinflowturbinespartiimultidisciplinaryoptimizationdesign |